First, we load, filter, and merge the data sets.
How does the data set looks like
Applied thresholds are indicated by grey horizontal line.
#Apply tresholds
data <- subset(data, Mean_Puncta_mitoTracker_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mitoTracker_Number_Object_Number < 1400)
data <- subset(data, mitoTracker_MeanArea < 0.04)
data <- subset(data, mitoTracker_MeanCount < 0.45)
data <- subset(data, mitoTracker_MeanLength < 0.03)
data <- subset(data, Branchpoints < 40)
#Save data set
write.csv(data, file = "results_Mito/tables/data_Mito.csv")
Cell counts per cell line:
#data <- read.csv("results_Mito/tables/data_Mito.csv")
table(data$Metadata_SampleID)
##
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015
## 180 487 572 363 239 362
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002
## 106 607 563 338 440 181
Mean cell count:
mean(table(data$Metadata_SampleID))
## [1] 369.8333
Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.
Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_AreaShape_Area ~ Disease_state + (1 |
## Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 41833.1 41858.6 -20912.5 41825.1 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3920 -0.6492 -0.2709 0.3061 5.8590
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 13.94 3.734
## Residual 721.25 26.856
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 39.4437 1.8016 21.893
## Disease_statesPD -0.7969 2.3663 -0.337
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_AreaShape_Area
## Chisq Df Pr(>Chisq)
## Disease_state 0.1134 1 0.7363
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Number_Object_Number ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 61269.8 61295.4 -30630.9 61261.8 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8442 -0.7792 -0.1781 0.6405 3.3980
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 4150 64.42
## Residual 57375 239.53
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 363.94 29.45 12.359
## Disease_statesPD -13.22 38.60 -0.342
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Number_Object_Number
## Chisq Df Pr(>Chisq)
## Disease_state 0.1173 1 0.732
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -5760.7 -5735.1 2884.4 -5768.7 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7557 -0.6566 -0.0335 0.6537 5.0985
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.01182 0.1087
## Residual 0.01572 0.1254
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.333999 0.048717 6.856
## Disease_statesPD -0.003415 0.063795 -0.054
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.764
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker
## Chisq Df Pr(>Chisq)
## Disease_state 0.0029 1 0.9573
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -30621.2 -30595.6 15314.6 -30629.2 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3575 -0.6898 -0.3120 0.3701 4.0149
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.739e-06 0.001319
## Residual 5.853e-05 0.007651
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0100208 0.0006207 16.143
## Disease_statesPD -0.0003579 0.0008147 -0.439
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanArea
## Chisq Df Pr(>Chisq)
## Disease_state 0.193 1 0.6604
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -9352.0 -9326.4 4680.0 -9360.0 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4454 -0.6933 -0.2872 0.4203 4.1937
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.0004919 0.02218
## Residual 0.0070443 0.08393
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0944784 0.0101454 9.312
## Disease_statesPD -0.0008737 0.0133005 -0.066
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanCount
## Chisq Df Pr(>Chisq)
## Disease_state 0.0043 1 0.9476
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 22131.7 22157.3 -11061.8 22123.7 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0710 -0.6804 -0.3376 0.3979 5.3016
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.2008 0.4481
## Residual 8.5103 2.9172
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.28202 0.21351 10.688
## Disease_statesPD 0.09162 0.28034 0.327
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.1068 1 0.7438
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 30642.9 30668.5 -15317.5 30634.9 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1527 -0.6508 -0.3941 0.2779 4.5745
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2.109 1.452
## Residual 57.860 7.607
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.5843 0.6774 8.243
## Disease_statesPD 0.3933 0.8888 0.443
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Branchpoints
## Chisq Df Pr(>Chisq)
## Disease_state 0.1958 1 0.6581
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 42834.4 42860.0 -21413.2 42826.4 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1404 -0.6018 -0.4357 0.2432 6.2086
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 29.46 5.427
## Residual 902.68 30.045
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 20.011 2.544 7.867
## Disease_statesPD 1.611 3.338 0.483
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.2329 1 0.6294
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -32577.1 -32551.5 16292.5 -32585.1 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0212 -0.6626 -0.3729 0.2750 4.4067
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.387e-06 0.001178
## Residual 3.765e-05 0.006136
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0040882 0.0005492 7.444
## Disease_statesPD 0.0006187 0.0007205 0.859
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanLength
## Chisq Df Pr(>Chisq)
## Disease_state 0.7372 1 0.3906